Position: Data Scientist (AML)
Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.
We are about giving customers a new way to spend, save and manage their money while taking better care of the planet which has seen us become a multi-award winning bank that now employs over 2800 across five offices in London, Cardiff, Dublin, Southampton, and Manchester. Our journey started in 2014, and since then we have surpassed 3.5 million accounts (and four account types!)
with 350,000 business customers. We are a fully licensed UK bank but at the heart, we are a tech first company, enabling our platform to deliver brilliant products.
Our technologists are at the very heart of Starling and enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do.
Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!
The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you:
From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values:
Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.
Hybrid Working
We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person. In Technology, we're asking that you attend the office a minimum of 1 day per week.
Our Data Environment
Our Data teams are excited about the value of data within the business, powers our product decisions to improve things for our customers and enhance effective and agile decision making, regardless of what their primary tech stack may be. Hear from the team in our latest blogs or our case studies with Women in Tech.
We are looking for talented data professionals at all levels to join the team. We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling. People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good.
Ways of Working:
• We value autonomy - you’ll be trusted to manage your own projects, drive modelling initiatives, and take ideas from concept to production
• You’ll be encouraged to propose new approaches and explore creative ways to detect and prevent fraud
• We debate and critique our ideas in a healthy, supportive team
• You’ll have the chance to shape both models and how we think about fraud detection as a wider team
Responsibilities:
• You will be part of a team that builds, evaluates and deploys machine learning models to improve and automate decision making
• Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions
• Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams
• Contribute to the development of end-to-end machine learning workflows and help embed models into production systems
• Analyse transaction and behavioural data to identify trends, anomalies, and AML patterns
• Industry experience in data science or machine learning models, ideally in AML, financial crime, or a related domain
• Experience working with large-scale, high-dimensional, and heavily imbalanced datasets
• Excellent skills in Python and SQL
• Solid understanding of classification algorithms such as gradient boosting decision trees, including pros and cons of…
